SlideShare a Scribd company logo

【DL輪読会】Segment Anything

Deep Learning JP
Deep Learning JP

SAM is a new segmentation model that can segment objects in images using natural language prompts. It was trained on over 1,100 datasets totaling over 10,000 images using a model-in-the-loop approach. SAM uses a transformer-based architecture with encoders for images, text, bounding boxes and masks. It achieves state-of-the-art zero-shot segmentation performance without any fine-tuning on target datasets.

1 of 24
Downloaded 159 times
Segment Anything Shohei Taniguchi, Matsuo Lab
Segment Anything ॻࢽ৘ใ ஶऀ Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick ֓ཁ • Meta͕ެ։ͨ͠ηάϝϯςʔγϣϯͷͨΊͷ‫ج‬൫ϞσϧSAM • 1100ສຕͷը૾ʹ10ԯҎ্ͷϚεΫ͕Ξϊςʔγϣϯ͞Εͨσʔληοτ SA-1B΋ެ։ 2
֓ཁ Segment-Anything Model,SAM • ༷ʑͳϓϩϯϓτ͔Β෺ମͷϚεΫΛੜ੒Ͱ͖ΔϞσϧ ࢦࣔ఺ɾςΩετɾྖҬͳͲ
֓ཁ Segment-Anything Model,SAM • Τοδ༧ଌ΍text-to-mask΋zero-shotͰ݁ߏͰ͖Δ
ൃද֓ཁ • λεΫɿPromotable segmentation • ϞσϧɿSegment Anything Model • σʔλɿData engine • ࣮‫ݧ‬ • ·ͱΊ 5
എ‫ܠ‬ • ۙ೥ɼେ‫ن‬໛‫ޠݴ‬Ϟσϧͷൃల͕͍͢͝ ‣ PromptΛ༩͑ͨΒࣗࡏʹ‫ޠݴ‬Λੜ੒Ͱ͖Δ ‣ Scaling lawͰͲΜͲΜੑೳ্͕͕Δ ➡ίϯϐϡʔλϏδϣϯͰ΋ಉ͡Α͏ͳ͜ͱ͸ Ͱ͖ͳ͍ͷ͔ʁ 6 https://j.gifs.com/Y7mBPW.gif
λεΫ Promptable Segmentation • ैདྷͷηάϝϯςʔγϣϯλεΫͱҧ͍ ηάϝϯτର৅ΛϓϩϯϓτͰࢦఆ͢Δ ‣ ࢦࣔ఺ɼྖҬɼςΩετͳͲ • ϓϩϯϓτ͸ᐆດੑΛ‫ؚ‬ΉͨΊ ਖ਼͍͠ϚεΫ͸1ͭͱ͸‫ݶ‬Βͳ͍ 7
Ϟσϧ Segment Anything Model,SAM • ߏ੒͸݁ߏγϯϓϧ 1. ը૾ͱϓϩϯϓτΛ ͦΕͧΕຒΊࠐΉ 2. TransformerϕʔεͷσίʔμͰ ຒΊࠐΈ͔ΒϚεΫΛੜ੒͢Δ 8
Ϟσϧ • Image encoder ‣ ը૾Λಛ௃ྔʹຒΊࠐΉ ‣ த਎͸ViT ‣ 1൪‫͕ࢉܭ‬ॏ͍෦෼͕ͩɼ ਪ࿦࣌ʹ͸ಛ௃ྔΛอ͓͚࣋ͯ͠͹ ϓϩϯϓτΛϦΞϧλΠϜͰ͍͡ΕΔ 9 Segment Anything Model,SAM
Ϟσϧ • Prompt encoder (points, box) ‣ ϓϩϯϓτΛຒΊࠐΉ ‣ positional encodingʹͯ͠ ֶशՄೳͳຒΊࠐΈύϥϝʔλͱ ଍͠߹ΘͤΔ 10 Segment Anything Model,SAM
Ϟσϧ • Prompt encoder (text) ‣ ϓϩϯϓτΛຒΊࠐΉ ‣ CLIPͷtext encoderΛ࢖͏ 11 Segment Anything Model,SAM
Ϟσϧ • Prompt encoder (mask) ‣ ϓϩϯϓτΛຒΊࠐΉ ‣ ৞ΈࠐΈΛ͔͚ͨ΋ͷΛ ը૾ຒΊࠐΈͱ଍͠߹ΘͤΔ 12 Segment Anything Model,SAM
Ϟσϧ • Mask decoder ‣ ϚεΫީิΛग़ྗ͢Δ ‣ த਎͸Transformerͷdecoder ‣ ϓϩϯϓτͷᐆດੑʹରॲ͢ΔͨΊʹ 3ͭͷީิΛग़ྗ͢Δ 13 Segment Anything Model,SAM
Ϟσϧ • ֶश ‣ Focal lossͱdice lossΛ ૊Έ߹Θֶͤͯश ‣ ϓϩϯϓτ͸ϥϯμϜʹ αϯϓϧ͢Δ 14 Segment Anything Model,SAM
σʔλ Data Engine • SAMΛΞϊςʔγϣϯʹ΋‫͢༻׆‬Δ ‣ Model-in-the-loop • 3ஈ֊ʹ෼͚ͯΞϊςʔγϣϯ͢Δ 15
1. SAM͕༧ଌͨ͠ϚεΫΛमਖ਼͢Δ • SAM͸ॳΊʹผͷσʔληοτͰ ࣄલʹֶश͓ͤͯ͘͞ • σʔλ͕͋Δఔ౓ू·ͬͨΒ ͦΕΛ࢖ͬͯSAMΛֶशͤ͞Δ • 1ը૾͋ͨΓ30ඵҎ಺ʹ෇༩Ͱ͖ΔൣғͰ Ξϊςʔγϣϯ 16 σʔλ Data Engine
2. SAM͕༧ଌͨ͠΋ͷҎ֎ΛΞϊςʔγϣϯ • ΑΓࡉ͔͍෦෼ΛΞϊςʔγϣϯ • ͜ͷࡍʹ΋৽͘͠௥Ճͨ͠σʔλͰ SAMΛֶशͤ͞Δ • ͜͜·ͰͰ1020ສ‫ݸ‬ͷϚεΫ͕ಘΒΕΔ 17 σʔλ Data Engine
3. SAMͷ༧ଌͰΞϊςʔγϣϯ • 2ஈ֊໨ͰSAM͕͔ͳΓ͍͍ਫ਼౓ʹ ͳ͍ͬͯΔͨΊɼ༧ଌ݁ՌΛ΄ͱΜͲ ͦͷ··Ξϊςʔγϣϯͱͯ͠࢖͑Δ • Ϟσϧͷ֬৴౓͕ߴ͍΋ͷΛબΜͰ NMSͰॏෳΛআ‫͢ڈ‬Δ 18 σʔλ Data Engine
σʔλ SA-1B • ࠷ऴతʹ1100ສຕͷը૾ʹ11ԯ‫ݸ‬ͷϚεΫ͕ ͍ͭͨσʔληοτ͕Ͱ͖Δ • ‫ط‬ଘͷσʔληοτʹൺ΂ͯɼ1ը૾͋ͨΓͷ ϚεΫͷ਺͕͍ͩͿଟ͍ 19
• ϚεΫͷҐஔͷόΠΞε΋গͳ͍ • ‫ط‬ଘͷ΋ͷ͸த৺෇ۙʹ͔ͳΓภ͍ͬͯΔ 20 σʔλ SA-1B
࣮‫ݧ‬ ࢦࣔ఺͔ΒͷϚεΫ༧ଌ • ଟ͘ͷϕϯνϚʔΫͰZero-shotͰ‫ط‬ଘͷϞσϧΛ্ճΔੑೳ͕ग़Δ • Zero-shotɿ֤σʔληοτͰfinetune͍ͯ͠ͳ͍ 21
࣮‫ݧ‬ ͦͷଞͷzero-shotੑೳ 22 Τοδ༧ଌ Text-to-mask
࣮‫ݧ‬ Ablation study • σʔλྔ΍ϞσϧαΠζʹΑͬͯੑೳ͕Ͳͷ͘Β͍มΘΔ͔ͷ෼ੳ • σʔλྔʹؔͯ͠͸100ສຕ͘Β͍Ͱ݁ߏανͬͯͦ͏ͳҹ৅
·ͱΊ • ϓϩϯϓτͰ੍‫ޚ‬Մೳͳηάϝϯςʔγϣϯ༻‫ج‬൫ϞσϧSAMΛఏҊ • SAMΛ࢖ͬͯmodel-in-the-loopͰσʔλΛऩूͨ͠SA-1Bσʔληοτ΋ެ։ • σϞ΋ެ։͞Ε͍ͯΔ https://segment-anything.com/demo • ϓϩϯϓτ͸ը૾‫Ͱܥ‬΋൚༻తʹ࢖͑ΔΞϓϩʔνʹͳΓͦ͏
Ad

Recommended

【メタサーベイ】基盤モデル / Foundation Models
【メタサーベイ】基盤モデル / Foundation Models【メタサーベイ】基盤モデル / Foundation Models
【メタサーベイ】基盤モデル / Foundation Models
cvpaper. challenge
 
ChatGPT 人間のフィードバックから強化学習した対話AI
ChatGPT 人間のフィードバックから強化学習した対話AIChatGPT 人間のフィードバックから強化学習した対話AI
ChatGPT 人間のフィードバックから強化学習した対話AI
Shota Imai
 
【DL輪読会】Flow Matching for Generative Modeling
【DL輪読会】Flow Matching for Generative Modeling【DL輪読会】Flow Matching for Generative Modeling
【DL輪読会】Flow Matching for Generative Modeling
Deep Learning JP
 
[DL輪読会]Learning Transferable Visual Models From Natural Language Supervision
[DL輪読会]Learning Transferable Visual Models From Natural Language Supervision[DL輪読会]Learning Transferable Visual Models From Natural Language Supervision
[DL輪読会]Learning Transferable Visual Models From Natural Language Supervision
Deep Learning JP
 
物体検出の歴史（R-CNNからSSD・YOLOまで）
物体検出の歴史（R-CNNからSSD・YOLOまで）物体検出の歴史（R-CNNからSSD・YOLOまで）
物体検出の歴史（R-CNNからSSD・YOLOまで）
HironoriKanazawa
 
しょぼいプレゼンをパワポのせいにするな! by @jessedee
しょぼいプレゼンをパワポのせいにするな! by @jessedeeしょぼいプレゼンをパワポのせいにするな! by @jessedee
しょぼいプレゼンをパワポのせいにするな! by @jessedee
「MakeLeaps」請求書の作成、管理、郵送
 
【DL輪読会】ViT + Self Supervised Learningまとめ
【DL輪読会】ViT + Self Supervised Learningまとめ【DL輪読会】ViT + Self Supervised Learningまとめ
【DL輪読会】ViT + Self Supervised Learningまとめ
Deep Learning JP
 
【DL輪読会】Visual Classification via Description from Large Language Models (ICLR...
【DL輪読会】Visual Classification via Description from Large Language Models (ICLR...【DL輪読会】Visual Classification via Description from Large Language Models (ICLR...
【DL輪読会】Visual Classification via Description from Large Language Models (ICLR...
Deep Learning JP
 
畳み込みニューラルネットワークの高精度化と高速化
畳み込みニューラルネットワークの高精度化と高速化畳み込みニューラルネットワークの高精度化と高速化
畳み込みニューラルネットワークの高精度化と高速化
Yusuke Uchida
 
Swin Transformer (ICCV'21 Best Paper) を完璧に理解する資料
Swin Transformer (ICCV'21 Best Paper) を完璧に理解する資料Swin Transformer (ICCV'21 Best Paper) を完璧に理解する資料
Swin Transformer (ICCV'21 Best Paper) を完璧に理解する資料
Yusuke Uchida
 
最適輸送入門
最適輸送入門最適輸送入門
最適輸送入門
joisino
 
SSII2021 [OS2-01] 転移学習の基礎：異なるタスクの知識を利用するための機械学習の方法
SSII2021 [OS2-01] 転移学習の基礎：異なるタスクの知識を利用するための機械学習の方法SSII2021 [OS2-01] 転移学習の基礎：異なるタスクの知識を利用するための機械学習の方法
SSII2021 [OS2-01] 転移学習の基礎：異なるタスクの知識を利用するための機械学習の方法
SSII
 
全力解説！Transformer
全力解説！Transformer全力解説！Transformer
全力解説！Transformer
Arithmer Inc.
 
実装レベルで学ぶVQVAE
実装レベルで学ぶVQVAE実装レベルで学ぶVQVAE
実装レベルで学ぶVQVAE
ぱんいち すみもと
 
【DL輪読会】言語以外でのTransformerのまとめ (ViT, Perceiver, Frozen Pretrained Transformer etc)
【DL輪読会】言語以外でのTransformerのまとめ (ViT, Perceiver, Frozen Pretrained Transformer etc)【DL輪読会】言語以外でのTransformerのまとめ (ViT, Perceiver, Frozen Pretrained Transformer etc)
【DL輪読会】言語以外でのTransformerのまとめ (ViT, Perceiver, Frozen Pretrained Transformer etc)
Deep Learning JP
 
最適輸送の解き方
最適輸送の解き方最適輸送の解き方
最適輸送の解き方
joisino
 
組合せ最適化入門：線形計画から整数計画まで
組合せ最適化入門：線形計画から整数計画まで組合せ最適化入門：線形計画から整数計画まで
組合せ最適化入門：線形計画から整数計画まで
Shunji Umetani
 
Optunaを使ったHuman-in-the-loop最適化の紹介 - 2023/04/27 W&B 東京ミートアップ #3
Optunaを使ったHuman-in-the-loop最適化の紹介 - 2023/04/27 W&B 東京ミートアップ #3Optunaを使ったHuman-in-the-loop最適化の紹介 - 2023/04/27 W&B 東京ミートアップ #3
Optunaを使ったHuman-in-the-loop最適化の紹介 - 2023/04/27 W&B 東京ミートアップ #3
Preferred Networks
 
最適化超入門
最適化超入門最適化超入門
最適化超入門
Takami Sato
 
backbone としての timm 入門
backbone としての timm 入門backbone としての timm 入門
backbone としての timm 入門
Takuji Tahara
 
Optimizer入門＆最新動向
Optimizer入門＆最新動向Optimizer入門＆最新動向
Optimizer入門＆最新動向
Motokawa Tetsuya
 
Skip Connection まとめ（Neural Network）
Skip Connection まとめ（Neural Network）Skip Connection まとめ（Neural Network）
Skip Connection まとめ（Neural Network）
Yamato OKAMOTO
 
生成モデルの Deep Learning
生成モデルの Deep Learning生成モデルの Deep Learning
生成モデルの Deep Learning
Seiya Tokui
 
[DL輪読会]Flow-based Deep Generative Models
[DL輪読会]Flow-based Deep Generative Models[DL輪読会]Flow-based Deep Generative Models
[DL輪読会]Flow-based Deep Generative Models
Deep Learning JP
 
【DL輪読会】Scaling Laws for Neural Language Models
【DL輪読会】Scaling Laws for Neural Language Models【DL輪読会】Scaling Laws for Neural Language Models
【DL輪読会】Scaling Laws for Neural Language Models
Deep Learning JP
 
【DL輪読会】ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
【DL輪読会】ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders【DL輪読会】ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
【DL輪読会】ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
Deep Learning JP
 
Tensor コアを使った PyTorch の高速化
Tensor コアを使った PyTorch の高速化Tensor コアを使った PyTorch の高速化
Tensor コアを使った PyTorch の高速化
Yusuke Fujimoto
 
Active Convolution, Deformable Convolution ―形状・スケールを学習可能なConvolution―
Active Convolution, Deformable Convolution ―形状・スケールを学習可能なConvolution―Active Convolution, Deformable Convolution ―形状・スケールを学習可能なConvolution―
Active Convolution, Deformable Convolution ―形状・スケールを学習可能なConvolution―
Yosuke Shinya
 
【DL輪読会】AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
【DL輪読会】AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners【DL輪読会】AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
【DL輪読会】AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
Deep Learning JP
 
【DL輪読会】事前学習用データセットについて
【DL輪読会】事前学習用データセットについて【DL輪読会】事前学習用データセットについて
【DL輪読会】事前学習用データセットについて
Deep Learning JP
 
Ad

More Related Content

What's hot (20)

畳み込みニューラルネットワークの高精度化と高速化
畳み込みニューラルネットワークの高精度化と高速化畳み込みニューラルネットワークの高精度化と高速化
畳み込みニューラルネットワークの高精度化と高速化
Yusuke Uchida
 
Swin Transformer (ICCV'21 Best Paper) を完璧に理解する資料
Swin Transformer (ICCV'21 Best Paper) を完璧に理解する資料Swin Transformer (ICCV'21 Best Paper) を完璧に理解する資料
Swin Transformer (ICCV'21 Best Paper) を完璧に理解する資料
Yusuke Uchida
 
最適輸送入門
最適輸送入門最適輸送入門
最適輸送入門
joisino
 
SSII2021 [OS2-01] 転移学習の基礎：異なるタスクの知識を利用するための機械学習の方法
SSII2021 [OS2-01] 転移学習の基礎：異なるタスクの知識を利用するための機械学習の方法SSII2021 [OS2-01] 転移学習の基礎：異なるタスクの知識を利用するための機械学習の方法
SSII2021 [OS2-01] 転移学習の基礎：異なるタスクの知識を利用するための機械学習の方法
SSII
 
全力解説！Transformer
全力解説！Transformer全力解説！Transformer
全力解説！Transformer
Arithmer Inc.
 
実装レベルで学ぶVQVAE
実装レベルで学ぶVQVAE実装レベルで学ぶVQVAE
実装レベルで学ぶVQVAE
ぱんいち すみもと
 
【DL輪読会】言語以外でのTransformerのまとめ (ViT, Perceiver, Frozen Pretrained Transformer etc)
【DL輪読会】言語以外でのTransformerのまとめ (ViT, Perceiver, Frozen Pretrained Transformer etc)【DL輪読会】言語以外でのTransformerのまとめ (ViT, Perceiver, Frozen Pretrained Transformer etc)
【DL輪読会】言語以外でのTransformerのまとめ (ViT, Perceiver, Frozen Pretrained Transformer etc)
Deep Learning JP
 
最適輸送の解き方
最適輸送の解き方最適輸送の解き方
最適輸送の解き方
joisino
 
組合せ最適化入門：線形計画から整数計画まで
組合せ最適化入門：線形計画から整数計画まで組合せ最適化入門：線形計画から整数計画まで
組合せ最適化入門：線形計画から整数計画まで
Shunji Umetani
 
Optunaを使ったHuman-in-the-loop最適化の紹介 - 2023/04/27 W&B 東京ミートアップ #3
Optunaを使ったHuman-in-the-loop最適化の紹介 - 2023/04/27 W&B 東京ミートアップ #3Optunaを使ったHuman-in-the-loop最適化の紹介 - 2023/04/27 W&B 東京ミートアップ #3
Optunaを使ったHuman-in-the-loop最適化の紹介 - 2023/04/27 W&B 東京ミートアップ #3
Preferred Networks
 
最適化超入門
最適化超入門最適化超入門
最適化超入門
Takami Sato
 
backbone としての timm 入門
backbone としての timm 入門backbone としての timm 入門
backbone としての timm 入門
Takuji Tahara
 
Optimizer入門＆最新動向
Optimizer入門＆最新動向Optimizer入門＆最新動向
Optimizer入門＆最新動向
Motokawa Tetsuya
 
Skip Connection まとめ（Neural Network）
Skip Connection まとめ（Neural Network）Skip Connection まとめ（Neural Network）
Skip Connection まとめ（Neural Network）
Yamato OKAMOTO
 
生成モデルの Deep Learning
生成モデルの Deep Learning生成モデルの Deep Learning
生成モデルの Deep Learning
Seiya Tokui
 
[DL輪読会]Flow-based Deep Generative Models
[DL輪読会]Flow-based Deep Generative Models[DL輪読会]Flow-based Deep Generative Models
[DL輪読会]Flow-based Deep Generative Models
Deep Learning JP
 
【DL輪読会】Scaling Laws for Neural Language Models
【DL輪読会】Scaling Laws for Neural Language Models【DL輪読会】Scaling Laws for Neural Language Models
【DL輪読会】Scaling Laws for Neural Language Models
Deep Learning JP
 
【DL輪読会】ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
【DL輪読会】ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders【DL輪読会】ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
【DL輪読会】ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
Deep Learning JP
 
Tensor コアを使った PyTorch の高速化
Tensor コアを使った PyTorch の高速化Tensor コアを使った PyTorch の高速化
Tensor コアを使った PyTorch の高速化
Yusuke Fujimoto
 
Active Convolution, Deformable Convolution ―形状・スケールを学習可能なConvolution―
Active Convolution, Deformable Convolution ―形状・スケールを学習可能なConvolution―Active Convolution, Deformable Convolution ―形状・スケールを学習可能なConvolution―
Active Convolution, Deformable Convolution ―形状・スケールを学習可能なConvolution―
Yosuke Shinya
 
畳み込みニューラルネットワークの高精度化と高速化
畳み込みニューラルネットワークの高精度化と高速化畳み込みニューラルネットワークの高精度化と高速化
畳み込みニューラルネットワークの高精度化と高速化
Yusuke Uchida
 
Swin Transformer (ICCV'21 Best Paper) を完璧に理解する資料
Swin Transformer (ICCV'21 Best Paper) を完璧に理解する資料Swin Transformer (ICCV'21 Best Paper) を完璧に理解する資料
Swin Transformer (ICCV'21 Best Paper) を完璧に理解する資料
Yusuke Uchida
 
最適輸送入門
最適輸送入門最適輸送入門
最適輸送入門
joisino
 
SSII2021 [OS2-01] 転移学習の基礎：異なるタスクの知識を利用するための機械学習の方法
SSII2021 [OS2-01] 転移学習の基礎：異なるタスクの知識を利用するための機械学習の方法SSII2021 [OS2-01] 転移学習の基礎：異なるタスクの知識を利用するための機械学習の方法
SSII2021 [OS2-01] 転移学習の基礎：異なるタスクの知識を利用するための機械学習の方法
SSII
 
全力解説！Transformer
全力解説！Transformer全力解説！Transformer
全力解説！Transformer
Arithmer Inc.
 
実装レベルで学ぶVQVAE
実装レベルで学ぶVQVAE実装レベルで学ぶVQVAE
実装レベルで学ぶVQVAE
ぱんいち すみもと
 
【DL輪読会】言語以外でのTransformerのまとめ (ViT, Perceiver, Frozen Pretrained Transformer etc)
【DL輪読会】言語以外でのTransformerのまとめ (ViT, Perceiver, Frozen Pretrained Transformer etc)【DL輪読会】言語以外でのTransformerのまとめ (ViT, Perceiver, Frozen Pretrained Transformer etc)
【DL輪読会】言語以外でのTransformerのまとめ (ViT, Perceiver, Frozen Pretrained Transformer etc)
Deep Learning JP
 
最適輸送の解き方
最適輸送の解き方最適輸送の解き方
最適輸送の解き方
joisino
 
組合せ最適化入門：線形計画から整数計画まで
組合せ最適化入門：線形計画から整数計画まで組合せ最適化入門：線形計画から整数計画まで
組合せ最適化入門：線形計画から整数計画まで
Shunji Umetani
 
Optunaを使ったHuman-in-the-loop最適化の紹介 - 2023/04/27 W&B 東京ミートアップ #3
Optunaを使ったHuman-in-the-loop最適化の紹介 - 2023/04/27 W&B 東京ミートアップ #3Optunaを使ったHuman-in-the-loop最適化の紹介 - 2023/04/27 W&B 東京ミートアップ #3
Optunaを使ったHuman-in-the-loop最適化の紹介 - 2023/04/27 W&B 東京ミートアップ #3
Preferred Networks
 
最適化超入門
最適化超入門最適化超入門
最適化超入門
Takami Sato
 
backbone としての timm 入門
backbone としての timm 入門backbone としての timm 入門
backbone としての timm 入門
Takuji Tahara
 
Optimizer入門＆最新動向
Optimizer入門＆最新動向Optimizer入門＆最新動向
Optimizer入門＆最新動向
Motokawa Tetsuya
 
Skip Connection まとめ（Neural Network）
Skip Connection まとめ（Neural Network）Skip Connection まとめ（Neural Network）
Skip Connection まとめ（Neural Network）
Yamato OKAMOTO
 
生成モデルの Deep Learning
生成モデルの Deep Learning生成モデルの Deep Learning
生成モデルの Deep Learning
Seiya Tokui
 
[DL輪読会]Flow-based Deep Generative Models
[DL輪読会]Flow-based Deep Generative Models[DL輪読会]Flow-based Deep Generative Models
[DL輪読会]Flow-based Deep Generative Models
Deep Learning JP
 
【DL輪読会】Scaling Laws for Neural Language Models
【DL輪読会】Scaling Laws for Neural Language Models【DL輪読会】Scaling Laws for Neural Language Models
【DL輪読会】Scaling Laws for Neural Language Models
Deep Learning JP
 
【DL輪読会】ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
【DL輪読会】ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders【DL輪読会】ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
【DL輪読会】ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
Deep Learning JP
 
Tensor コアを使った PyTorch の高速化
Tensor コアを使った PyTorch の高速化Tensor コアを使った PyTorch の高速化
Tensor コアを使った PyTorch の高速化
Yusuke Fujimoto
 
Active Convolution, Deformable Convolution ―形状・スケールを学習可能なConvolution―
Active Convolution, Deformable Convolution ―形状・スケールを学習可能なConvolution―Active Convolution, Deformable Convolution ―形状・スケールを学習可能なConvolution―
Active Convolution, Deformable Convolution ―形状・スケールを学習可能なConvolution―
Yosuke Shinya
 

More from Deep Learning JP (20)

【DL輪読会】AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
【DL輪読会】AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners【DL輪読会】AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
【DL輪読会】AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
Deep Learning JP
 
【DL輪読会】事前学習用データセットについて
【DL輪読会】事前学習用データセットについて【DL輪読会】事前学習用データセットについて
【DL輪読会】事前学習用データセットについて
Deep Learning JP
 
【DL輪読会】 "Learning to render novel views from wide-baseline stereo pairs." CVP...
【DL輪読会】 "Learning to render novel views from wide-baseline stereo pairs." CVP...【DL輪読会】 "Learning to render novel views from wide-baseline stereo pairs." CVP...
【DL輪読会】 "Learning to render novel views from wide-baseline stereo pairs." CVP...
Deep Learning JP
 
【DL輪読会】Zero-Shot Dual-Lens Super-Resolution
【DL輪読会】Zero-Shot Dual-Lens Super-Resolution【DL輪読会】Zero-Shot Dual-Lens Super-Resolution
【DL輪読会】Zero-Shot Dual-Lens Super-Resolution
Deep Learning JP
 
【DL輪読会】BloombergGPT: A Large Language Model for Finance arxiv
【DL輪読会】BloombergGPT: A Large Language Model for Finance arxiv【DL輪読会】BloombergGPT: A Large Language Model for Finance arxiv
【DL輪読会】BloombergGPT: A Large Language Model for Finance arxiv
Deep Learning JP
 
【DL輪読会】マルチモーダル LLM
【DL輪読会】マルチモーダル LLM【DL輪読会】マルチモーダル LLM
【DL輪読会】マルチモーダル LLM
Deep Learning JP
 
【 DL輪読会】ToolLLM: Facilitating Large Language Models to Master 16000+ Real-wo...
【 DL輪読会】ToolLLM: Facilitating Large Language Models to Master 16000+ Real-wo... 【 DL輪読会】ToolLLM: Facilitating Large Language Models to Master 16000+ Real-wo...
【 DL輪読会】ToolLLM: Facilitating Large Language Models to Master 16000+ Real-wo...
Deep Learning JP
 
【DL輪読会】AnyLoc: Towards Universal Visual Place Recognition
【DL輪読会】AnyLoc: Towards Universal Visual Place Recognition【DL輪読会】AnyLoc: Towards Universal Visual Place Recognition
【DL輪読会】AnyLoc: Towards Universal Visual Place Recognition
Deep Learning JP
 
【DL輪読会】Can Neural Network Memorization Be Localized?
【DL輪読会】Can Neural Network Memorization Be Localized?【DL輪読会】Can Neural Network Memorization Be Localized?
【DL輪読会】Can Neural Network Memorization Be Localized?
Deep Learning JP
 
【DL輪読会】Hopfield network　関連研究について
【DL輪読会】Hopfield network　関連研究について【DL輪読会】Hopfield network　関連研究について
【DL輪読会】Hopfield network　関連研究について
Deep Learning JP
 
【DL輪読会】SimPer: Simple self-supervised learning of periodic targets( ICLR 2023 )
【DL輪読会】SimPer: Simple self-supervised learning of periodic targets( ICLR 2023 )【DL輪読会】SimPer: Simple self-supervised learning of periodic targets( ICLR 2023 )
【DL輪読会】SimPer: Simple self-supervised learning of periodic targets( ICLR 2023 )
Deep Learning JP
 
【DL輪読会】RLCD: Reinforcement Learning from Contrast Distillation for Language M...
【DL輪読会】RLCD: Reinforcement Learning from Contrast Distillation for Language M...【DL輪読会】RLCD: Reinforcement Learning from Contrast Distillation for Language M...
【DL輪読会】RLCD: Reinforcement Learning from Contrast Distillation for Language M...
Deep Learning JP
 
【DL輪読会】"Secrets of RLHF in Large Language Models Part I: PPO"
【DL輪読会】"Secrets of RLHF in Large Language Models Part I: PPO"【DL輪読会】"Secrets of RLHF in Large Language Models Part I: PPO"
【DL輪読会】"Secrets of RLHF in Large Language Models Part I: PPO"
Deep Learning JP
 
【DL輪読会】"Language Instructed Reinforcement Learning for Human-AI Coordination "
【DL輪読会】"Language Instructed Reinforcement Learning for Human-AI Coordination "【DL輪読会】"Language Instructed Reinforcement Learning for Human-AI Coordination "
【DL輪読会】"Language Instructed Reinforcement Learning for Human-AI Coordination "
Deep Learning JP
 
【DL輪読会】Llama 2: Open Foundation and Fine-Tuned Chat Models
【DL輪読会】Llama 2: Open Foundation and Fine-Tuned Chat Models【DL輪読会】Llama 2: Open Foundation and Fine-Tuned Chat Models
【DL輪読会】Llama 2: Open Foundation and Fine-Tuned Chat Models
Deep Learning JP
 
【DL輪読会】"Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware"
【DL輪読会】"Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware"【DL輪読会】"Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware"
【DL輪読会】"Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware"
Deep Learning JP
 
【DL輪読会】Parameter is Not All You Need:Starting from Non-Parametric Networks fo...
【DL輪読会】Parameter is Not All You Need:Starting from Non-Parametric Networks fo...【DL輪読会】Parameter is Not All You Need:Starting from Non-Parametric Networks fo...
【DL輪読会】Parameter is Not All You Need:Starting from Non-Parametric Networks fo...
Deep Learning JP
 
【DL輪読会】Drag Your GAN: Interactive Point-based Manipulation on the Generative ...
【DL輪読会】Drag Your GAN: Interactive Point-based Manipulation on the Generative ...【DL輪読会】Drag Your GAN: Interactive Point-based Manipulation on the Generative ...
【DL輪読会】Drag Your GAN: Interactive Point-based Manipulation on the Generative ...
Deep Learning JP
 
【DL輪読会】Self-Supervised Learning from Images with a Joint-Embedding Predictive...
【DL輪読会】Self-Supervised Learning from Images with a Joint-Embedding Predictive...【DL輪読会】Self-Supervised Learning from Images with a Joint-Embedding Predictive...
【DL輪読会】Self-Supervised Learning from Images with a Joint-Embedding Predictive...
Deep Learning JP
 
【DL輪読会】Towards Understanding Ensemble, Knowledge Distillation and Self-Distil...
【DL輪読会】Towards Understanding Ensemble, Knowledge Distillation and Self-Distil...【DL輪読会】Towards Understanding Ensemble, Knowledge Distillation and Self-Distil...
【DL輪読会】Towards Understanding Ensemble, Knowledge Distillation and Self-Distil...
Deep Learning JP
 
【DL輪読会】AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
【DL輪読会】AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners【DL輪読会】AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
【DL輪読会】AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
Deep Learning JP
 
【DL輪読会】事前学習用データセットについて
【DL輪読会】事前学習用データセットについて【DL輪読会】事前学習用データセットについて
【DL輪読会】事前学習用データセットについて
Deep Learning JP
 
【DL輪読会】 "Learning to render novel views from wide-baseline stereo pairs." CVP...
【DL輪読会】 "Learning to render novel views from wide-baseline stereo pairs." CVP...【DL輪読会】 "Learning to render novel views from wide-baseline stereo pairs." CVP...
【DL輪読会】 "Learning to render novel views from wide-baseline stereo pairs." CVP...
Deep Learning JP
 
【DL輪読会】Zero-Shot Dual-Lens Super-Resolution
【DL輪読会】Zero-Shot Dual-Lens Super-Resolution【DL輪読会】Zero-Shot Dual-Lens Super-Resolution
【DL輪読会】Zero-Shot Dual-Lens Super-Resolution
Deep Learning JP
 
【DL輪読会】BloombergGPT: A Large Language Model for Finance arxiv
【DL輪読会】BloombergGPT: A Large Language Model for Finance arxiv【DL輪読会】BloombergGPT: A Large Language Model for Finance arxiv
【DL輪読会】BloombergGPT: A Large Language Model for Finance arxiv
Deep Learning JP
 
【DL輪読会】マルチモーダル LLM
【DL輪読会】マルチモーダル LLM【DL輪読会】マルチモーダル LLM
【DL輪読会】マルチモーダル LLM
Deep Learning JP
 
【 DL輪読会】ToolLLM: Facilitating Large Language Models to Master 16000+ Real-wo...
【 DL輪読会】ToolLLM: Facilitating Large Language Models to Master 16000+ Real-wo... 【 DL輪読会】ToolLLM: Facilitating Large Language Models to Master 16000+ Real-wo...
【 DL輪読会】ToolLLM: Facilitating Large Language Models to Master 16000+ Real-wo...
Deep Learning JP
 
【DL輪読会】AnyLoc: Towards Universal Visual Place Recognition
【DL輪読会】AnyLoc: Towards Universal Visual Place Recognition【DL輪読会】AnyLoc: Towards Universal Visual Place Recognition
【DL輪読会】AnyLoc: Towards Universal Visual Place Recognition
Deep Learning JP
 
【DL輪読会】Can Neural Network Memorization Be Localized?
【DL輪読会】Can Neural Network Memorization Be Localized?【DL輪読会】Can Neural Network Memorization Be Localized?
【DL輪読会】Can Neural Network Memorization Be Localized?
Deep Learning JP
 
【DL輪読会】Hopfield network　関連研究について
【DL輪読会】Hopfield network　関連研究について【DL輪読会】Hopfield network　関連研究について
【DL輪読会】Hopfield network　関連研究について
Deep Learning JP
 
【DL輪読会】SimPer: Simple self-supervised learning of periodic targets( ICLR 2023 )
【DL輪読会】SimPer: Simple self-supervised learning of periodic targets( ICLR 2023 )【DL輪読会】SimPer: Simple self-supervised learning of periodic targets( ICLR 2023 )
【DL輪読会】SimPer: Simple self-supervised learning of periodic targets( ICLR 2023 )
Deep Learning JP
 
【DL輪読会】RLCD: Reinforcement Learning from Contrast Distillation for Language M...
【DL輪読会】RLCD: Reinforcement Learning from Contrast Distillation for Language M...【DL輪読会】RLCD: Reinforcement Learning from Contrast Distillation for Language M...
【DL輪読会】RLCD: Reinforcement Learning from Contrast Distillation for Language M...
Deep Learning JP
 
【DL輪読会】"Secrets of RLHF in Large Language Models Part I: PPO"
【DL輪読会】"Secrets of RLHF in Large Language Models Part I: PPO"【DL輪読会】"Secrets of RLHF in Large Language Models Part I: PPO"
【DL輪読会】"Secrets of RLHF in Large Language Models Part I: PPO"
Deep Learning JP
 
【DL輪読会】"Language Instructed Reinforcement Learning for Human-AI Coordination "
【DL輪読会】"Language Instructed Reinforcement Learning for Human-AI Coordination "【DL輪読会】"Language Instructed Reinforcement Learning for Human-AI Coordination "
【DL輪読会】"Language Instructed Reinforcement Learning for Human-AI Coordination "
Deep Learning JP
 
【DL輪読会】Llama 2: Open Foundation and Fine-Tuned Chat Models
【DL輪読会】Llama 2: Open Foundation and Fine-Tuned Chat Models【DL輪読会】Llama 2: Open Foundation and Fine-Tuned Chat Models
【DL輪読会】Llama 2: Open Foundation and Fine-Tuned Chat Models
Deep Learning JP
 
【DL輪読会】"Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware"
【DL輪読会】"Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware"【DL輪読会】"Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware"
【DL輪読会】"Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware"
Deep Learning JP
 
【DL輪読会】Parameter is Not All You Need:Starting from Non-Parametric Networks fo...
【DL輪読会】Parameter is Not All You Need:Starting from Non-Parametric Networks fo...【DL輪読会】Parameter is Not All You Need:Starting from Non-Parametric Networks fo...
【DL輪読会】Parameter is Not All You Need:Starting from Non-Parametric Networks fo...
Deep Learning JP
 
【DL輪読会】Drag Your GAN: Interactive Point-based Manipulation on the Generative ...
【DL輪読会】Drag Your GAN: Interactive Point-based Manipulation on the Generative ...【DL輪読会】Drag Your GAN: Interactive Point-based Manipulation on the Generative ...
【DL輪読会】Drag Your GAN: Interactive Point-based Manipulation on the Generative ...
Deep Learning JP
 
【DL輪読会】Self-Supervised Learning from Images with a Joint-Embedding Predictive...
【DL輪読会】Self-Supervised Learning from Images with a Joint-Embedding Predictive...【DL輪読会】Self-Supervised Learning from Images with a Joint-Embedding Predictive...
【DL輪読会】Self-Supervised Learning from Images with a Joint-Embedding Predictive...
Deep Learning JP
 
【DL輪読会】Towards Understanding Ensemble, Knowledge Distillation and Self-Distil...
【DL輪読会】Towards Understanding Ensemble, Knowledge Distillation and Self-Distil...【DL輪読会】Towards Understanding Ensemble, Knowledge Distillation and Self-Distil...
【DL輪読会】Towards Understanding Ensemble, Knowledge Distillation and Self-Distil...
Deep Learning JP
 
Ad

Recently uploaded (20)

Manifest Pre-Seed Update | A Humanoid OEM Deeptech In France
Manifest Pre-Seed Update | A Humanoid OEM Deeptech In FranceManifest Pre-Seed Update | A Humanoid OEM Deeptech In France
Manifest Pre-Seed Update | A Humanoid OEM Deeptech In France
chb3
 
Generative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in BusinessGenerative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in Business
Dr. Tathagat Varma
 
Special Meetup Edition - TDX Bengaluru Meetup #52.pptx
Special Meetup Edition - TDX Bengaluru Meetup #52.pptxSpecial Meetup Edition - TDX Bengaluru Meetup #52.pptx
Special Meetup Edition - TDX Bengaluru Meetup #52.pptx
shyamraj55
 
Splunk Security Update | Public Sector Summit Germany 2025
Splunk Security Update | Public Sector Summit Germany 2025Splunk Security Update | Public Sector Summit Germany 2025
Splunk Security Update | Public Sector Summit Germany 2025
Splunk
 
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...
Alan Dix
 
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
organizerofv
 
Dev Dives: Automate and orchestrate your processes with UiPath Maestro
Dev Dives: Automate and orchestrate your processes with UiPath MaestroDev Dives: Automate and orchestrate your processes with UiPath Maestro
Dev Dives: Automate and orchestrate your processes with UiPath Maestro
UiPathCommunity
 
How analogue intelligence complements AI
How analogue intelligence complements AIHow analogue intelligence complements AI
How analogue intelligence complements AI
Paul Rowe
 
Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...
Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...
Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...
Aqusag Technologies
 
Procurement Insights Cost To Value Guide.pptx
Procurement Insights Cost To Value Guide.pptxProcurement Insights Cost To Value Guide.pptx
Procurement Insights Cost To Value Guide.pptx
Jon Hansen
 
Semantic Cultivators : The Critical Future Role to Enable AI
Semantic Cultivators : The Critical Future Role to Enable AISemantic Cultivators : The Critical Future Role to Enable AI
Semantic Cultivators : The Critical Future Role to Enable AI
artmondano
 
ThousandEyes Partner Innovation Updates for May 2025
ThousandEyes Partner Innovation Updates for May 2025ThousandEyes Partner Innovation Updates for May 2025
ThousandEyes Partner Innovation Updates for May 2025
ThousandEyes
 
HCL Nomad Web – Best Practices and Managing Multiuser Environments
HCL Nomad Web – Best Practices and Managing Multiuser EnvironmentsHCL Nomad Web – Best Practices and Managing Multiuser Environments
HCL Nomad Web – Best Practices and Managing Multiuser Environments
panagenda
 
Cyber Awareness overview for 2025 month of security
Cyber Awareness overview for 2025 month of securityCyber Awareness overview for 2025 month of security
Cyber Awareness overview for 2025 month of security
riccardosl1
 
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptxIncreasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Anoop Ashok
 
Rusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond SparkRusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond Spark
carlyakerly1
 
Drupalcamp Finland – Measuring Front-end Energy Consumption
Drupalcamp Finland – Measuring Front-end Energy ConsumptionDrupalcamp Finland – Measuring Front-end Energy Consumption
Drupalcamp Finland – Measuring Front-end Energy Consumption
Exove
 
Big Data Analytics Quick Research Guide by Arthur Morgan
Big Data Analytics Quick Research Guide by Arthur MorganBig Data Analytics Quick Research Guide by Arthur Morgan
Big Data Analytics Quick Research Guide by Arthur Morgan
Arthur Morgan
 
Linux Support for SMARC: How Toradex Empowers Embedded Developers
Linux Support for SMARC: How Toradex Empowers Embedded DevelopersLinux Support for SMARC: How Toradex Empowers Embedded Developers
Linux Support for SMARC: How Toradex Empowers Embedded Developers
Toradex
 
AI and Data Privacy in 2025: Global Trends
AI and Data Privacy in 2025: Global TrendsAI and Data Privacy in 2025: Global Trends
AI and Data Privacy in 2025: Global Trends
InData Labs
 
Manifest Pre-Seed Update | A Humanoid OEM Deeptech In France
Manifest Pre-Seed Update | A Humanoid OEM Deeptech In FranceManifest Pre-Seed Update | A Humanoid OEM Deeptech In France
Manifest Pre-Seed Update | A Humanoid OEM Deeptech In France
chb3
 
Generative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in BusinessGenerative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in Business
Dr. Tathagat Varma
 
Special Meetup Edition - TDX Bengaluru Meetup #52.pptx
Special Meetup Edition - TDX Bengaluru Meetup #52.pptxSpecial Meetup Edition - TDX Bengaluru Meetup #52.pptx
Special Meetup Edition - TDX Bengaluru Meetup #52.pptx
shyamraj55
 
Splunk Security Update | Public Sector Summit Germany 2025
Splunk Security Update | Public Sector Summit Germany 2025Splunk Security Update | Public Sector Summit Germany 2025
Splunk Security Update | Public Sector Summit Germany 2025
Splunk
 
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...
Alan Dix
 
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
organizerofv
 
Dev Dives: Automate and orchestrate your processes with UiPath Maestro
Dev Dives: Automate and orchestrate your processes with UiPath MaestroDev Dives: Automate and orchestrate your processes with UiPath Maestro
Dev Dives: Automate and orchestrate your processes with UiPath Maestro
UiPathCommunity
 
How analogue intelligence complements AI
How analogue intelligence complements AIHow analogue intelligence complements AI
How analogue intelligence complements AI
Paul Rowe
 
Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...
Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...
Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...
Aqusag Technologies
 
Procurement Insights Cost To Value Guide.pptx
Procurement Insights Cost To Value Guide.pptxProcurement Insights Cost To Value Guide.pptx
Procurement Insights Cost To Value Guide.pptx
Jon Hansen
 
Semantic Cultivators : The Critical Future Role to Enable AI
Semantic Cultivators : The Critical Future Role to Enable AISemantic Cultivators : The Critical Future Role to Enable AI
Semantic Cultivators : The Critical Future Role to Enable AI
artmondano
 
ThousandEyes Partner Innovation Updates for May 2025
ThousandEyes Partner Innovation Updates for May 2025ThousandEyes Partner Innovation Updates for May 2025
ThousandEyes Partner Innovation Updates for May 2025
ThousandEyes
 
HCL Nomad Web – Best Practices and Managing Multiuser Environments
HCL Nomad Web – Best Practices and Managing Multiuser EnvironmentsHCL Nomad Web – Best Practices and Managing Multiuser Environments
HCL Nomad Web – Best Practices and Managing Multiuser Environments
panagenda
 
Cyber Awareness overview for 2025 month of security
Cyber Awareness overview for 2025 month of securityCyber Awareness overview for 2025 month of security
Cyber Awareness overview for 2025 month of security
riccardosl1
 
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptxIncreasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Anoop Ashok
 
Rusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond SparkRusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond Spark
carlyakerly1
 
Drupalcamp Finland – Measuring Front-end Energy Consumption
Drupalcamp Finland – Measuring Front-end Energy ConsumptionDrupalcamp Finland – Measuring Front-end Energy Consumption
Drupalcamp Finland – Measuring Front-end Energy Consumption
Exove
 
Big Data Analytics Quick Research Guide by Arthur Morgan
Big Data Analytics Quick Research Guide by Arthur MorganBig Data Analytics Quick Research Guide by Arthur Morgan
Big Data Analytics Quick Research Guide by Arthur Morgan
Arthur Morgan
 
Linux Support for SMARC: How Toradex Empowers Embedded Developers
Linux Support for SMARC: How Toradex Empowers Embedded DevelopersLinux Support for SMARC: How Toradex Empowers Embedded Developers
Linux Support for SMARC: How Toradex Empowers Embedded Developers
Toradex
 
AI and Data Privacy in 2025: Global Trends
AI and Data Privacy in 2025: Global TrendsAI and Data Privacy in 2025: Global Trends
AI and Data Privacy in 2025: Global Trends
InData Labs
 
Ad

【DL輪読会】Segment Anything